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Zhao Y, Li S, He X, Yu J, Zhang L, Zhang H, Wei D, Wang B, Li J, Guo H, He X. Liver injury monitoring using dynamic fluorescence molecular tomography based on a time-energy difference strategy. BIOMEDICAL OPTICS EXPRESS 2023; 14:5298-5315. [PMID: 37854546 PMCID: PMC10581805 DOI: 10.1364/boe.498092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 09/03/2023] [Accepted: 09/05/2023] [Indexed: 10/20/2023]
Abstract
Dynamic fluorescence molecular tomography (DFMT) is a promising molecular imaging technique that offers the potential to monitor fast kinetic behaviors within small animals in three dimensions. Early monitoring of liver disease requires the ability to distinguish and analyze normal and injured liver tissues. However, the inherent ill-posed nature of the problem and energy signal interference between the normal and injured liver regions limit the practical application of liver injury monitoring. In this study, we propose a novel strategy based on time and energy, leveraging the temporal correlation in fluorescence molecular imaging (FMI) sequences and the metabolic differences between normal and injured liver tissue. Additionally, considering fluorescence signal distribution disparity between the injured and normal regions, we designed a universal Golden Ratio Primal-Dual Algorithm (GRPDA) to reconstruct both the normal and injured liver regions. Numerical simulation and in vivo experiment results demonstrate that the proposed strategy can effectively avoid signal interference between liver and liver injury energy and lead to significant improvements in morphology recovery and positioning accuracy compared to existing approaches. Our research presents a new perspective on distinguishing normal and injured liver tissues for early liver injury monitoring.
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Affiliation(s)
- Yizhe Zhao
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China
| | - Shuangchen Li
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China
| | - Xuelei He
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China
| | - Jingjing Yu
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, 710062, China
| | - Lizhi Zhang
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China
| | - Heng Zhang
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China
| | - De Wei
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China
| | - Beilei Wang
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China
| | - Jintao Li
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China
| | - Hongbo Guo
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China
| | - Xiaowei He
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- School of Information Sciences and Technology, Northwest University, Xi'an, 710127, China
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Zhang J, Zhang G, Chen Y, Li K, Zhao F, Yi H, Su L, Cao X. Regularized reconstruction based on joint smoothly clipped absolute deviation regularization and graph manifold learning for fluorescence molecular tomography. Phys Med Biol 2023; 68:195004. [PMID: 37647921 DOI: 10.1088/1361-6560/acf55a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 08/30/2023] [Indexed: 09/01/2023]
Abstract
Objective.Fluorescence molecular tomography (FMT) is an optical imaging modality that provides high sensitivity and low cost, which can offer the three-dimensional distribution of biomarkers by detecting the fluorescently labeled probe noninvasively. In the field of preclinical cancer diagnosis and treatment, FMT has gained significant traction. Nonetheless, the current FMT reconstruction results suffer from unsatisfactory morphology and location accuracy of the fluorescence distribution, primarily due to the light scattering effect and the ill-posed nature of the inverse problem.Approach.To address these challenges, a regularized reconstruction method based on joint smoothly clipped absolute deviation regularization and graph manifold learning (SCAD-GML) for FMT is presented in this paper. The SCAD-GML approach combines the sparsity of the fluorescent sources with the latent manifold structure of fluorescent source distribution to achieve more accurate and sparse reconstruction results. To obtain the reconstruction results efficiently, the non-convex gradient descent iterative method is employed to solve the established objective function. To assess the performance of the proposed SCAD-GML method, a comprehensive evaluation is conducted through numerical simulation experiments as well asin vivoexperiments.Main results.The results demonstrate that the SCAD-GML method outperforms other methods in terms of both location and shape recovery of fluorescence biomarkers distribution.Siginificance.These findings indicate that the SCAD-GML method has the potential to advance the application of FMT inin vivobiological research.
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Affiliation(s)
- Jun Zhang
- School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, People's Republic of China
- National and Local Joint Engineering Research Center for Cultural Heritage Digitization, Xi'an, Shaanxi 710127, People's Republic of China
| | - Gege Zhang
- School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, People's Republic of China
- National and Local Joint Engineering Research Center for Cultural Heritage Digitization, Xi'an, Shaanxi 710127, People's Republic of China
| | - Yi Chen
- School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, People's Republic of China
- National and Local Joint Engineering Research Center for Cultural Heritage Digitization, Xi'an, Shaanxi 710127, People's Republic of China
| | - Kang Li
- School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, People's Republic of China
- National and Local Joint Engineering Research Center for Cultural Heritage Digitization, Xi'an, Shaanxi 710127, People's Republic of China
| | - Fengjun Zhao
- School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, People's Republic of China
| | - Huangjian Yi
- School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, People's Republic of China
| | - Linzhi Su
- School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, People's Republic of China
- National and Local Joint Engineering Research Center for Cultural Heritage Digitization, Xi'an, Shaanxi 710127, People's Republic of China
| | - Xin Cao
- School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, People's Republic of China
- National and Local Joint Engineering Research Center for Cultural Heritage Digitization, Xi'an, Shaanxi 710127, People's Republic of China
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Zhang L, Guo H, Li J, Kang D, Zhang D, He X, Zhao Y, Wei D, Yu J. Multi-target reconstruction strategy based on blind source separation of surface measurement signals in FMT. BIOMEDICAL OPTICS EXPRESS 2023; 14:1159-1177. [PMID: 36950247 PMCID: PMC10026579 DOI: 10.1364/boe.481348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 01/30/2023] [Accepted: 02/07/2023] [Indexed: 06/18/2023]
Abstract
Fluorescence molecular tomography (FMT) is a promising molecular imaging technique for tumor detection in the early stage. High-precision multi-target reconstructions are necessary for quantitative analysis in practical FMT applications. The existing reconstruction methods perform well in retrieving a single fluorescent target but may fail in reconstructing a multi-target, which remains an obstacle to the wider application of FMT. In this paper, a novel multi-target reconstruction strategy based on blind source separation (BSS) of surface measurement signals was proposed, which transformed the multi-target reconstruction problem into multiple single-target reconstruction problems. Firstly, by multiple points excitation, multiple groups of superimposed measurement signals conforming to the conditions of BSS were constructed. Secondly, an efficient nonnegative least-correlated component analysis with iterative volume maximization (nLCA-IVM) algorithm was applied to construct the separation matrix, and the superimposed measurement signals were separated into the measurements of each target. Thirdly, the least squares fitting method was combined with BSS to determine the number of fluorophores indirectly. Lastly, each target was reconstructed based on the extracted surface measurement signals. Numerical simulations and in vivo experiments proved that it has the ability of multi-target resolution for FMT. The encouraging results demonstrate the significant effectiveness and potential of our method for practical FMT applications.
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Affiliation(s)
- Lizhi Zhang
- The Xi’an Key Laboratory of Radiomics and Intelligent Perception, Xi’an, China
- School of Information Sciences and Technology, Northwest University, Xi’an 710127, China
| | - Hongbo Guo
- The Xi’an Key Laboratory of Radiomics and Intelligent Perception, Xi’an, China
- School of Information Sciences and Technology, Northwest University, Xi’an 710127, China
| | - Jintao Li
- The Xi’an Key Laboratory of Radiomics and Intelligent Perception, Xi’an, China
- School of Information Sciences and Technology, Northwest University, Xi’an 710127, China
| | - Dizhen Kang
- The Xi’an Key Laboratory of Radiomics and Intelligent Perception, Xi’an, China
- School of Information Sciences and Technology, Northwest University, Xi’an 710127, China
| | - Diya Zhang
- The Xi’an Key Laboratory of Radiomics and Intelligent Perception, Xi’an, China
- School of Information Sciences and Technology, Northwest University, Xi’an 710127, China
| | - Xiaowei He
- The Xi’an Key Laboratory of Radiomics and Intelligent Perception, Xi’an, China
- School of Information Sciences and Technology, Northwest University, Xi’an 710127, China
| | - Yizhe Zhao
- The Xi’an Key Laboratory of Radiomics and Intelligent Perception, Xi’an, China
- School of Information Sciences and Technology, Northwest University, Xi’an 710127, China
| | - De Wei
- The Xi’an Key Laboratory of Radiomics and Intelligent Perception, Xi’an, China
- School of Information Sciences and Technology, Northwest University, Xi’an 710127, China
| | - Jingjing Yu
- School of Physics and Information Technology, Shaanxi Normal University, Xi’an 710062, China
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Cao C, Xiao A, Cai M, Shen B, Guo L, Shi X, Tian J, Hu Z. Excitation-based fully connected network for precise NIR-II fluorescence molecular tomography. BIOMEDICAL OPTICS EXPRESS 2022; 13:6284-6299. [PMID: 36589575 PMCID: PMC9774866 DOI: 10.1364/boe.474982] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 10/26/2022] [Accepted: 10/30/2022] [Indexed: 06/17/2023]
Abstract
Fluorescence molecular tomography (FMT) is a novel imaging modality to obtain fluorescence biomarkers' three-dimensional (3D) distribution. However, the simplified mathematical model and complicated inverse problem limit it to achieving precise results. In this study, the second near-infrared (NIR-II) fluorescence imaging was adopted to mitigate tissue scattering and reduce noise interference. An excitation-based fully connected network was proposed to model the inverse process of NIR-II photon propagation and directly obtain the 3D distribution of the light source. An excitation block was embedded in the network allowing it to autonomously pay more attention to neurons related to the light source. The barycenter error was added to the loss function to improve the localization accuracy of the light source. Both numerical simulation and in vivo experiments showed the superiority of the novel NIR-II FMT reconstruction strategy over the baseline methods. This strategy was expected to facilitate the application of machine learning in biomedical research.
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Affiliation(s)
- Caiguang Cao
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
- These authors contributed equally
| | - Anqi Xiao
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
- These authors contributed equally
| | - Meishan Cai
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Biluo Shen
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Lishuang Guo
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, China
| | - Xiaojing Shi
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, China
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China
| | - Zhenhua Hu
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
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Arridge SR, Ehrhardt MJ, Thielemans K. (An overview of) Synergistic reconstruction for multimodality/multichannel imaging methods. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200205. [PMID: 33966461 DOI: 10.1098/rsta.2020.0205] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Imaging is omnipresent in modern society with imaging devices based on a zoo of physical principles, probing a specimen across different wavelengths, energies and time. Recent years have seen a change in the imaging landscape with more and more imaging devices combining that which previously was used separately. Motivated by these hardware developments, an ever increasing set of mathematical ideas is appearing regarding how data from different imaging modalities or channels can be synergistically combined in the image reconstruction process, exploiting structural and/or functional correlations between the multiple images. Here we review these developments, give pointers to important challenges and provide an outlook as to how the field may develop in the forthcoming years. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 1'.
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Affiliation(s)
- Simon R Arridge
- Department of Computer Science, University College London, London, UK
| | - Matthias J Ehrhardt
- Department of Mathematical Sciences, University of Bath, Bath, UK
- Institute for Mathematical Innovation, University of Bath, Bath, UK
| | - Kris Thielemans
- Institute of Nuclear Medicine, University College London, London, UK
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Meng H, Gao Y, Yang X, Wang K, Tian J. K-Nearest Neighbor Based Locally Connected Network for Fast Morphological Reconstruction in Fluorescence Molecular Tomography. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:3019-3028. [PMID: 32286961 DOI: 10.1109/tmi.2020.2984557] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Fluorescence molecular tomography (FMT) is a highly sensitive and noninvasive imaging modality for three-dimensional visualization of fluorescence probe distribution in small animals. However, the simplified photon propagation model and ill-posed inverse problem limit the improvement of FMT reconstruction. In this work, we proposed a novel K-nearest neighbor based locally connected (KNN-LC) network to improve the performance of morphological reconstruction in FMT. It directly builds the inverse process of photon transmission by learning the mapping relation between the surface photon intensity and the distribution of fluorescent source. KNN-LC network cascades a fully connected (FC) sub-network with a locally connected (LC) sub-network, where the FC part provides a coarse reconstruction result and LC part fine-tunes the morphological quality of reconstructed result. To assess the performance of our proposed network, we implemented both numerical simulation and in vivo studies. Furthermore, split Bregman-resolved total variation (SBRTV) regularization method and inverse problem simulation (IPS) method were utilized as baselines in all comparisons. The results demonstrated that KNN-LC network achieved accurate reconstruction in both source localization and morphology recovery in a short time. This promoted the in vivo application of FMT for visualizing the distribution of biomarkers inside biological tissue.
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Cao X, Li K, Xu XL, Deneen KMV, Geng GH, Chen XL. Development of tomographic reconstruction for three-dimensional optical imaging: From the inversion of light propagation to artificial intelligence. Artif Intell Med Imaging 2020; 1:78-86. [DOI: 10.35711/aimi.v1.i2.78] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 08/01/2020] [Accepted: 08/21/2020] [Indexed: 02/06/2023] Open
Abstract
Optical molecular tomography (OMT) is an imaging modality which uses an optical signal, especially near-infrared light, to reconstruct the three-dimensional information of the light source in biological tissue. With the advantages of being low-cost, noninvasive and having high sensitivity, OMT has been applied in preclinical and clinical research. However, due to its serious ill-posedness and ill-condition, the solution of OMT requires heavy data analysis and the reconstruction quality is limited. Recently, the artificial intelligence (commonly known as AI)-based methods have been proposed to provide a different tool to solve the OMT problem. In this paper, we review the progress on OMT algorithms, from conventional methods to AI-based methods, and we also give a prospective towards future developments in this domain.
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Affiliation(s)
- Xin Cao
- School of Information Science and Technology, Northwest University, Xi’an 710069, Shaanxi Province, China
| | - Kang Li
- School of Information Science and Technology, Northwest University, Xi’an 710069, Shaanxi Province, China
| | - Xue-Li Xu
- School of Information Science and Technology, Northwest University, Xi’an 710069, Shaanxi Province, China
| | - Karen M von Deneen
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, and School of Life Science and Technology, Xidian University, Xi’an 710126, Shaanxi Province, China
| | - Guo-Hua Geng
- School of Information Science and Technology, Northwest University, Xi’an 710069, Shaanxi Province, China
| | - Xue-Li Chen
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, and School of Life Science and Technology, Xidian University, Xi’an 710126, Shaanxi Province, China
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An Y, Meng H, Gao Y, Tong T, Zhang C, Wang K, Tian J. Application of machine learning method in optical molecular imaging: a review. SCIENCE CHINA INFORMATION SCIENCES 2020; 63:111101. [DOI: 10.1007/s11432-019-2708-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Revised: 09/17/2019] [Accepted: 10/22/2019] [Indexed: 08/30/2023]
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Meng H, Wang K, Gao Y, Jin Y, Ma X, Tian J. Adaptive Gaussian Weighted Laplace Prior Regularization Enables Accurate Morphological Reconstruction in Fluorescence Molecular Tomography. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:2726-2734. [PMID: 31021763 DOI: 10.1109/tmi.2019.2912222] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Fluorescence molecular tomography (FMT), as a powerful imaging technique in preclinical research, can offer the three-dimensional distribution of biomarkers by detecting the fluorescently labelled probe noninvasively. However, because of the light scattering effect and the ill-pose of inverse problem, it is challenging to develop an efficient reconstruction method, which can provide accurate location and morphology of the fluorescence distribution. In this research, we proposed a novel adaptive Gaussian weighted Laplace prior (AGWLP) regularization method, which assumed the variance of fluorescence intensity between any two voxels had a non-linear correlation with their Gaussian distance. It utilized an adaptive Gaussian kernel parameter strategy to achieve accurate morphological reconstructions in FMT. To evaluate the performance of the AGWLP method, we conducted numerical simulation and in vivo experiments. The results were compared with fast iterative shrinkage (FIS) thresholding method, split Bregman-resolved TV (SBRTV) regularization method, and Gaussian weighted Laplace prior (GWLP) regularization method. We validated in vivo imaging results against planar fluorescence images of frozen sections. The results demonstrated that the AGWLP method achieved superior performance in both location and shape recovery of fluorescence distribution. This enabled FMT more suitable and practical for in vivo visualization of biomarkers.
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Bland J, Mehranian A, Belzunce MA, Ellis S, da Costa‐Luis C, McGinnity CJ, Hammers A, Reader AJ. Intercomparison of MR-informed PET image reconstruction methods. Med Phys 2019; 46:5055-5074. [PMID: 31494961 PMCID: PMC6899618 DOI: 10.1002/mp.13812] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2019] [Revised: 08/23/2019] [Accepted: 08/23/2019] [Indexed: 12/28/2022] Open
Abstract
PURPOSE Numerous image reconstruction methodologies for positron emission tomography (PET) have been developed that incorporate magnetic resonance (MR) imaging structural information, producing reconstructed images with improved suppression of noise and reduced partial volume effects. However, the influence of MR structural information also increases the possibility of suppression or bias of structures present only in the PET data (PET-unique regions). To address this, further developments for MR-informed methods have been proposed, for example, through inclusion of the current reconstructed PET image, alongside the MR image, in the iterative reconstruction process. In this present work, a number of kernel and maximum a posteriori (MAP) methodologies are compared, with the aim of identifying methods that enable a favorable trade-off between the suppression of noise and the retention of unique features present in the PET data. METHODS The reconstruction methods investigated were: the MR-informed conventional and spatially compact kernel methods, referred to as KEM and KEM largest value sparsification (LVS) respectively; the MR-informed Bowsher and Gaussian MR-guided MAP methods; and the PET-MR-informed hybrid kernel and anato-functional MAP methods. The trade-off between improving the reconstruction of the whole brain region and the PET-unique regions was investigated for all methods in comparison with postsmoothed maximum likelihood expectation maximization (MLEM), evaluated in terms of structural similarity index (SSIM), normalized root mean square error (NRMSE), bias, and standard deviation. Both simulated BrainWeb (10 noise realizations) and real [18 F] fluorodeoxyglucose (FDG) three-dimensional datasets were used. The real [18 F]FDG dataset was augmented with simulated tumors to allow comparison of the reconstruction methodologies for the case of known regions of PET-MR discrepancy and evaluated at full counts (100%) and at a reduced (10%) count level. RESULTS For the high-count simulated and real data studies, the anato-functional MAP method performed better than the other methods under investigation (MR-informed, PET-MR-informed and postsmoothed MLEM), in terms of achieving the best trade-off for the reconstruction of the whole brain and PET-unique regions, assessed in terms of the SSIM, NRMSE, and bias vs standard deviation. The inclusion of PET information in the anato-functional MAP method enables the reconstruction of PET-unique regions to attain similarly low levels of bias as unsmoothed MLEM, while moderately improving the whole brain image quality for low levels of regularization. However, for low count simulated datasets the anato-functional MAP method performs poorly, due to the inclusion of noisy PET information in the regularization term. For the low counts simulated dataset, KEM LVS and to a lesser extent, HKEM performed better than the other methods under investigation in terms of achieving the best trade-off for the reconstruction of the whole brain and PET-unique regions, assessed in terms of the SSIM, NRMSE, and bias vs standard deviation. CONCLUSION For the reconstruction of noisy data, multiple MR-informed methods produce favorable whole brain vs PET-unique region trade-off in terms of the image quality metrics of SSIM and NRMSE, comfortably outperforming the whole image denoising of postsmoothed MLEM.
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Affiliation(s)
- James Bland
- School of Biomedical Engineering and Imaging SciencesKing's College LondonSt Thomas' HospitalLondonSE1 7EHUK
| | - Abolfazl Mehranian
- School of Biomedical Engineering and Imaging SciencesKing's College LondonSt Thomas' HospitalLondonSE1 7EHUK
| | - Martin A. Belzunce
- School of Biomedical Engineering and Imaging SciencesKing's College LondonSt Thomas' HospitalLondonSE1 7EHUK
| | - Sam Ellis
- School of Biomedical Engineering and Imaging SciencesKing's College LondonSt Thomas' HospitalLondonSE1 7EHUK
| | - Casper da Costa‐Luis
- School of Biomedical Engineering and Imaging SciencesKing's College LondonSt Thomas' HospitalLondonSE1 7EHUK
| | - Colm J. McGinnity
- King's College London & Guy's and St Thomas' PET CentreSt Thomas' HospitalLondonSE1 7EHUK
| | - Alexander Hammers
- King's College London & Guy's and St Thomas' PET CentreSt Thomas' HospitalLondonSE1 7EHUK
| | - Andrew J. Reader
- School of Biomedical Engineering and Imaging SciencesKing's College LondonSt Thomas' HospitalLondonSE1 7EHUK
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High Temporal-Resolution Dynamic PET Image Reconstruction Using a New Spatiotemporal Kernel Method. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:664-674. [PMID: 30222553 PMCID: PMC6422751 DOI: 10.1109/tmi.2018.2869868] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Current clinical dynamic PET has an effective temporal resolution of 5-10 seconds, which can be adequate for traditional compartmental modeling but is inadequate for exploiting the benefit of more advanced tracer kinetic modeling for characterization of diseases (e.g., cancer and heart disease). There is a need to improve dynamic PET to allow fine temporal sampling of 1-2 seconds. However, the reconstruction of these short-time frames from tomographic data is extremely challenging as the count level of each frame is very low and high noise presents in both spatial and temporal domains. Previously, the kernel framework has been developed and demonstrated as a statistically efficient approach to utilizing image prior for low-count PET image reconstruction. Nevertheless, the existing kernel methods mainly explore spatial correlations in the data and only have a limited ability in suppressing temporal noise. In this paper, we propose a new kernel method which extends the previous spatial kernel method to the general spatiotemporal domain. The new kernelized model encodes both spatial and temporal correlations obtained from image prior information and are incorporated into the PET forward projection model to improve themaximumlikelihood(ML) image reconstruction. Computer simulations and an application to real patient scan have shown that the proposed approach can achieve effective noise reduction in both spatial and temporal domains and outperform the spatial kernel method and conventional ML reconstruction method for improving the high temporal-resolution dynamic PET imaging.
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Liu T, Rong J, Gao P, Pu H, Zhang W, Zhang X, Liang Z, Lu H. Regularized reconstruction based on joint L 1 and total variation for sparse-view cone-beam X-ray luminescence computed tomography. BIOMEDICAL OPTICS EXPRESS 2019; 10:1-17. [PMID: 30775079 PMCID: PMC6363206 DOI: 10.1364/boe.10.000001] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Revised: 11/22/2018] [Accepted: 11/22/2018] [Indexed: 05/22/2023]
Abstract
As an emerging hybrid imaging modality, cone-beam X-ray luminescence computed tomography (CB-XLCT) has been proposed based on the development of X-ray excitable nanoparticles. Owing to the high degree of absorption and scattering of light through tissues, the CB-XLCT inverse problem is inherently ill-conditioned. Appropriate priors or regularizations are needed to facilitate reconstruction and to restrict the search space to a specific solution set. Typically, the goal of CB-XLCT reconstruction is to get the distributions of nanophosphors in the imaging object. Considering that the distributions of nanophosphors inside bodies preferentially accumulate in specific areas of interest, the reconstruction of XLCT images is usually sparse with some locally smoothed high-intensity regions. Therefore, a combination of the L1 and total variation regularization is designed to improve the imaging quality of CB-XLCT in this study. The L1 regularization is used for enforcing the sparsity of the reconstructed images and the total variation regularization is used for maintaining the local smoothness of the reconstructed image. The implementation of this method can be divided into two parts. First, the reconstruction image was reconstructed based on the fast iterative shrinkage-thresholding (FISTA) algorithm, then the reconstruction image was minimized by the gradient descent method. Numerical simulations and phantom experiments indicate that compared with the traditional ART, ADAPTIK and FISTA methods, the proposed method demonstrates its advantage in improving spatial resolution and reducing imaging time.
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Affiliation(s)
- Tianshuai Liu
- Department of Biomedical Engineering, Fourth Military Medical University, Xi’an, Shaanxi 710032, China
| | - Junyan Rong
- Department of Biomedical Engineering, Fourth Military Medical University, Xi’an, Shaanxi 710032, China
| | - Peng Gao
- Department of Biomedical Engineering, Fourth Military Medical University, Xi’an, Shaanxi 710032, China
| | - Huangsheng Pu
- Department of Biomedical Engineering, Fourth Military Medical University, Xi’an, Shaanxi 710032, China
| | - Wenli Zhang
- Department of Biomedical Engineering, Fourth Military Medical University, Xi’an, Shaanxi 710032, China
| | - Xiaofeng Zhang
- Department of Biomedical Engineering, Fourth Military Medical University, Xi’an, Shaanxi 710032, China
| | - Zhengrong Liang
- Department of Radiology and Biomedical Engineering, State University of New York at Stony Brook, Stony Brook, NY 11794, USA
| | - Hongbing Lu
- Department of Biomedical Engineering, Fourth Military Medical University, Xi’an, Shaanxi 710032, China
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An Y, Wang K, Tian J. Recent methodology advances in fluorescence molecular tomography. Vis Comput Ind Biomed Art 2018; 1:1. [PMID: 32240398 PMCID: PMC7098398 DOI: 10.1186/s42492-018-0001-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2018] [Accepted: 06/30/2018] [Indexed: 12/26/2022] Open
Abstract
Molecular imaging (MI) is a novel imaging discipline that has been continuously developed in recent years. It combines biochemistry, multimodal imaging, biomathematics, bioinformatics, cell & molecular physiology, biophysics, and pharmacology, and it provides a new technology platform for the early diagnosis and quantitative analysis of diseases, treatment monitoring and evaluation, and the development of comprehensive physiology. Fluorescence Molecular Tomography (FMT) is a type of optical imaging modality in MI that captures the three-dimensional distribution of fluorescence within a biological tissue generated by a specific molecule of fluorescent material within a biological tissue. Compared with other optical molecular imaging methods, FMT has the characteristics of high sensitivity, low cost, and safety and reliability. It has become the research frontier and research hotspot of optical molecular imaging technology. This paper took an overview of the recent methodology advances in FMT, mainly focused on the photon propagation model of FMT based on the radiative transfer equation (RTE), and the reconstruction problem solution consist of forward problem and inverse problem. We introduce the detailed technologies utilized in reconstruction of FMT. Finally, the challenges in FMT were discussed. This survey aims at summarizing current research hotspots in methodology of FMT, from which future research may benefit.
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Affiliation(s)
- Yu An
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Kun Wang
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jie Tian
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
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14
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Bland J, Belzunce MA, Ellis S, McGinnity CJ, Hammers A, Reader AJ. Spatially-Compact MR-Guided Kernel EM for PET Image Reconstruction. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2018; 2:470-482. [PMID: 30298139 PMCID: PMC6173308 DOI: 10.1109/trpms.2018.2844559] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Positron emission tomography (PET) is a highly sensitive functional and molecular imaging modality which can measure picomolar concentrations of an injected radionuclide. However, the physical sensitivity of PET is limited, and reducing the injected dose leads to low count data and noisy reconstructed images. A highly effective way of reducing noise is to reparameterise the reconstruction in terms of MR-derived spatial basis functions. Spatial basis functions derived using the kernel method have demonstrated excellent noise reduction properties and maintain shared PET-MR detailed structures. However, as previously shown in the literature, the MR-guided kernel method may lead to excessive smoothing of structures that are only present in the PET data. This work makes two main contributions in order to address this problem: first, we exploit the potential of the MR-guided kernel method to form more spatially-compact basis functions which are able to preserve PET-unique structures, and secondly, we consider reconstruction at the native MR resolution. The former contribution notably improves the recovery of structures which are unique to the PET data. These adaptations of the kernel method were compared to the conventional implementation of the MR-guided kernel method and also to MLEM, in terms of ability to recover PET unique structures for both simulated and real data. The spatially-compact kernel method showed clear visual and quantitative improvements in the reconstruction of the PET unique structures, relative to the conventional kernel method for all sizes of PET unique structures investigated, whilst maintaining to a large extent the impressive noise mitigating and detail preserving properties of the conventional MR-guided kernel method. We therefore conclude that a spatially-compact parameterisation of the MR-guided kernel method, should be the preferred implementation strategy in order to obviate unnecessary losses in PET-unique details.
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Affiliation(s)
- James Bland
- King's College London, St Thomas' Hospital, London, U.K
| | | | - Sam Ellis
- King's College London, St Thomas' Hospital, London, U.K
| | - Colm J McGinnity
- King's College London & Guy's and St Thomas' PET Centre, St Thomas' Hospital, London, U.K
| | - Alexander Hammers
- King's College London & Guy's and St Thomas' PET Centre, St Thomas' Hospital, London, U.K
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15
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Bland J, Mehranian A, Belzunce MA, Ellis S, McGinnity CJ, Hammers A, Reader AJ. MR-Guided Kernel EM Reconstruction for Reduced Dose PET Imaging. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2018; 2:235-243. [PMID: 29978142 PMCID: PMC6027990 DOI: 10.1109/trpms.2017.2771490] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
PET image reconstruction is highly susceptible to the impact of Poisson noise, and if shorter acquisition times or reduced injected doses are used, the noisy PET data become even more limiting. The recent development of kernel expectation maximisation (KEM) is a simple way to reduce noise in PET images, and we show in this work that impressive dose reduction can be achieved when the kernel method is used with MR-derived kernels. The kernel method is shown to surpass maximum likelihood expectation maximisation (MLEM) for the reconstruction of low-count datasets (corresponding to those obtained at reduced injected doses) producing visibly clearer reconstructions for unsmoothed and smoothed images, at all count levels. The kernel EM reconstruction of 10% of the data had comparable whole brain voxel-level error measures to the MLEM reconstruction of 100% of the data (for simulated data, at 100 iterations). For regional metrics, the kernel method at reduced dose levels attained a reduced coefficient of variation and more accurate mean values compared to MLEM. However, the advances provided by the kernel method are at the expense of possible over-smoothing of features unique to the PET data. Further assessment on clinical data is required to determine the level of dose reduction that can be routinely achieved using the kernel method, whilst maintaining the diagnostic utility of the scan.
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Affiliation(s)
- James Bland
- King's College London, St Thomas' Hospital, London, U.K
| | | | | | - Sam Ellis
- King's College London, St Thomas' Hospital, London, U.K
| | - Colm J McGinnity
- King's College London & Guy's and St Thomas' PET Centre, St Thomas' Hospital, London, U.K
| | - Alexander Hammers
- King's College London & Guy's and St Thomas' PET Centre, St Thomas' Hospital, London, U.K
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